Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,29 +1,41 @@
 import os
 import tempfile
-import streamlit as st
-from llama_cpp import Llama
+from pathlib import Path
 
 """
-Quick‑start **no‑frills, no explicit caching** version.
-
-* Creates a _one‑off_ temp folder (writable) for Hugging Face without
-  trying to pre‑create directories.
-* No `@st.cache_resource` – the model initialises every run so you can
-  at least confirm it loads. (You can add caching back once this works.)
-* Uses `global llm` so we don’t re‑download weights on every button
-  click within the same session.
+**Streamlit Space – minimal version (no explicit caching)**
+
+Fixes the `StreamlitSetPageConfigMustBeFirstCommandError` **and** the
+previous permission issues by:
+
+1. Setting `HOME`, `HF_HOME`, and `HF_CACHE_DIR` **before** we import
+   Streamlit – that way Streamlit creates `~/.streamlit` in a writable
+   place instead of `/.streamlit`.
+2. Moving the call to `st.set_page_config()` to be the **very first
+   Streamlit command**.
+
+You can re‑add `@st.cache_resource` once everything boots.
 """
 
-# A writable directory that *already exists*
-HF_TMP = tempfile.mkdtemp(prefix="hf_")
+# ----------------------------------------------------------------------------
+# Writable environment — must be set *before* importing streamlit
+# ----------------------------------------------------------------------------
+HF_TMP = tempfile.mkdtemp(prefix="hf_")  # guaranteed‑writable temp dir
+os.environ["HOME"] = HF_TMP  # for ~/.streamlit
 os.environ["HF_HOME"] = HF_TMP
-os.environ["HF_CACHE_DIR"] = HF_TMP  # where weights land
+os.environ["HF_CACHE_DIR"] = HF_TMP
 
-# ------------------------------------------------------------
-# Model loader (no Streamlit cache)
-# ------------------------------------------------------------
+# ----------------------------------------------------------------------------
+import streamlit as st
+from llama_cpp import Llama
 
-llm = None  # will be set on first use
+# ----- Streamlit page config (must be first Streamlit command) -------------
+st.set_page_config(page_title="Sanskrit Transliteration", page_icon="📜")
+
+# ----------------------------------------------------------------------------
+# Model loader (no Streamlit cache for now)
+# ----------------------------------------------------------------------------
+llm = None  # global singleton
 
 def get_model():
     global llm
@@ -32,13 +44,13 @@ def get_model():
             llm = Llama.from_pretrained(
                 repo_id="13Aluminium/Sanskrit_transliteration_Qwen3",
                 filename="unsloth.Q4_K_M.gguf",
-                cache_dir=HF_TMP,
+                cache_dir=os.environ["HF_CACHE_DIR"],
             )
     return llm
 
-# ------------------------------------------------------------
+# ----------------------------------------------------------------------------
 # Inference helper
-# ------------------------------------------------------------
+# ----------------------------------------------------------------------------
 
 def transliterate(text: str, *, temperature: float = 0.1, max_tokens: int = 200) -> str:
     prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
@@ -55,11 +67,10 @@ Transliterate Sanskrit Shlok to Latin
     out = model(prompt, max_tokens=max_tokens, temperature=temperature, echo=False)
     return out["choices"][0]["text"].strip()
 
-# ------------------------------------------------------------
-# Streamlit UI
-# ------------------------------------------------------------
+# ----------------------------------------------------------------------------
+# UI
+# ----------------------------------------------------------------------------
 
-st.set_page_config(page_title="Sanskrit Transliteration", page_icon="📜")
 st.title("📜 Sanskrit → Latin Transliteration (Qwen3)")
 
 sample = (
@@ -81,7 +92,9 @@ if st.button("🔤 Transliterate"):
             result = transliterate(sanskrit_text, temperature=temp, max_tokens=max_tokens)
         st.text(result)
 
-# requirements.txt – keep the same as before:
+# ----------------------------------------------------------------------------
+# requirements.txt – keep the same:
 # streamlit>=1.36
 # llama-cpp-python>=0.2.11
 # huggingface_hub>=0.24
+# ----------------------------------------------------------------------------